Associate Process Manager

Role summary Build the GenAI and agentic AI solution. This role combines LLM engineering, agent orchestration, RAG implementation, backend APIs, evaluation, deployment, and production support. Hire three people under this common JD, but ensure the team collectively covers agent workflows, RAG / knowledge systems, and platform / AgentOps.

Key responsibilities

• Build agentic workflows: Implement agent behavior, prompts, tool/function calling, structured outputs, state management, memory, handoffs, and human approval checkpoints.
• Implement RAG systems: Build document ingestion, parsing, chunking, embeddings, vector indexes, hybrid search, re-ranking, metadata filtering, and citation generation.
• Develop backend services: Create production APIs and services using Python, FastAPI or similar frameworks, integrated with LLM providers and enterprise systems.
• Integrate enterprise tools: Connect agents with business APIs, databases, search systems, workflow tools, CRM, ERP, ticketing systems, and collaboration platforms.
• Create evaluation pipelines: Build test datasets, prompt regression tests, retrieval quality checks, LLM-as-judge evaluations, and human feedback loops.
• Support deployment and operations: Containerize services, participate in CI/CD, configure monitoring, trace agent runs, track cost and latency, and troubleshoot production issues.
• Improve quality and safety: Implement guardrails, input/output validation, prompt injection defenses, access controls, audit logging, and fallback handling.

Required skills and experience

• Strong Python engineering skills with production-quality coding practices, Git, testing, packaging, and code reviews.
• Hands-on experience with LLM APIs and frameworks such as OpenAI, Azure OpenAI, Anthropic, Gemini, Bedrock, LangGraph, LangChain, LlamaIndex, Semantic Kernel, CrewAI, or similar.
• Practical understanding of prompt engineering, structured outputs, tool/function calling, agent orchestration, memory/state, and multi-step reasoning workflows.
• Experience with RAG, embeddings, vector databases, document ingestion, search relevance, metadata filters, and retrieval evaluation.
• Backend development experience with FastAPI, Flask, Django, or similar API frameworks.
• Experience with Docker, CI/CD, cloud deployment, secrets management, logging, monitoring, and environment configuration.